{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://www.luxonis.com/logo.svg\" width=\"400\">\n",
    "\n",
    "# Conversion of PyTorch Model\n",
    "\n",
    "## 🌟 Overview\n",
    "In this tutorial, we'll go through converting a pre-trained PyTorch model. We'll first download the model, test its inference, and export it to ONNX format. We'll then make it ready for deployment on a Luxonis device and finally test it on a device.\n",
    "\n",
    "## 📜 Table of Contents\n",
    "- [🛠️ Installation](#installation)\n",
    "- [🗃️ Model Download](#model-download)\n",
    "- [✍ Model Test (Optional)](#model-test)\n",
    "- [📦 NN Archive](#nn-archive)\n",
    "- [🗂️ Export and Archive](#export-and-archive)\n",
    "- [🤖 Deploy](#deploy)\n",
    "- [📷 DepthAI Script](#depthai-script)\n",
    "- [🗂️ Export without Archive (Optional)](#onnx-export)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"installation\"></a>\n",
    "\n",
    "## 🛠️ Installation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main focus of this tutorial is using [`ModelConverter`](https://github.com/luxonis/modelconverter) for conversion of a pre-trained model [`ResNet-18`](https://pytorch.org/vision/stable/models/generated/torchvision.models.quantization.resnet18.html#resnet18) from `torchvision` to formats supported by Luxonis devices. `ModelConverter` is our open-source tool that supports conversion to all RVC Compiled Formats. Furthermore, we'll also use [`LuxonisML`](https://github.com/luxonis/luxonis-ml) since it provides us with functionality to generate a [`NN Archive`](https://rvc4.docs.luxonis.com/software/ai-inference/nn-archive/). Finally, we will use [`DepthAI v3`](https://rvc4.docs.luxonis.com/software/) and [`DepthaAI Nodes`](https://rvc4.docs.luxonis.com/software/ai-inference/depthai-nodes/) to run the converted model, process and visualize the results. So, let's not wait any longer and get straight to it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install -q torchvision torch onnx pillow opencv-python modelconv@git+https://github.com/luxonis/modelconverter.git@main -U\n",
    "%pip install --extra-index-url https://artifacts.luxonis.com/artifactory/luxonis-python-release-local/ depthai==3.0.0a10\n",
    "%pip install -q depthai-nodes@git+https://github.com/luxonis/depthai-nodes.git@main -U"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"model-download\"></a>\n",
    "\n",
    "## 🗃️ Model Download"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's download the model from `torchvision`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu): ReLU(inplace=True)\n",
       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (layer1): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=512, out_features=1000, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torchvision\n",
    "\n",
    "# Load the pretrained ResNet-18 model\n",
    "model = torchvision.models.resnet18(weights=\"IMAGENET1K_V1\")\n",
    "model.eval()  # Set the model to evaluation mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"model-test\"></a>\n",
    "\n",
    "## ✍ Model Test (Optional)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's a good practice to verify the performance of a source model that we want to convert to know that the model is working. This way, when the model is exported and isn't performing well on a device, we know that the problem must lie in the conversion process. \n",
    "\n",
    "We will test the inference of the model on an image of a cat from a public dataset called [`crawford/cat-dataset`](https://www.kaggle.com/datasets/crawford/cat-dataset) available on Kaggle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import IPython\n",
    "\n",
    "img_file = \"media/cat.jpg\"\n",
    "\n",
    "# Show the image\n",
    "IPython.display.Image(img_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top-5 predictions:\n",
      "1) tiger cat: 66.76%\n",
      "2) tabby cat: 15.32%\n",
      "3) lynx: 8.52%\n",
      "4) Egyptian Mau: 4.93%\n",
      "5) Persian cat: 0.60%\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "from PIL import Image\n",
    "import torch\n",
    "\n",
    "#  Load the image\n",
    "image = Image.open(img_file).convert(\"RGB\")\n",
    "# Define the transformation                                      \n",
    "transforms = torchvision.models.ResNet18_Weights.IMAGENET1K_V1.transforms(antialias=True)\n",
    "\n",
    "# Preprocess the image\n",
    "input_tensor = transforms(image).unsqueeze(0)  # Add batch dimension\n",
    "\n",
    "# Perform inference\n",
    "with torch.no_grad():\n",
    "    output = model(input_tensor)\n",
    "    probabilities = torch.nn.functional.softmax(output[0], dim=0)\n",
    "\n",
    "# Get the top-5 predictions\n",
    "_, indices = torch.topk(probabilities, 5)\n",
    "\n",
    "# Load the labels\n",
    "# Downloaded from: https://raw.githubusercontent.com/anishathalye/imagenet-simple-labels/master/imagenet-simple-labels.json\n",
    "with open('media/imagenet-simple-labels.json', 'r') as f:\n",
    "    labels = json.load(f)\n",
    "\n",
    "print(\"Top-5 predictions:\")\n",
    "for i, idx in enumerate(indices, start=1):\n",
    "    print(f\"{i}) {labels[idx]}: {probabilities[idx].item()*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have verified that the model returns reasonable predictions, so let's jump into the conversion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"nn-archive\"></a>\n",
    "\n",
    "## 📦 NN Archive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section will introduce [`NN Archive`](https://rvc4.docs.luxonis.com/software/ai-inference/nn-archive/), what it is, and its benefit. `NN Archive` is our own format that packages the model executable(s) and configuration files into a .tar.xz archive. The primary purpose of the `NN Archive` is to describe and specify what the model expects as an input, what the model outputs, and lastly, if and how to process the result. The benefit of the `NN Archive` is seamless integration with our library ecosystem, especially the `DepthAI Nodes` package responsible for processing a model's output. Later in this tutorial, we will experience the benefit of this ourselves.\n",
    "\n",
    "We will use functions from [`LuxonisML`](https://github.com/luxonis/luxonis-ml) to create the `NN Archive`. The `NN Archive` consists of two parts, model executables (e.g. `ONNX`, `OpenVINO IR`, `TFLite`) and a config encoding the scheme version and a dictionary describing a model's inputs, outputs, heads, and metadata sections. Let's briefly describe each section.\n",
    "\n",
    "**Inputs**\n",
    "\n",
    "This section describes all of the model's input(s) and their preprocessing. It's defined as a list of dictionaries. To check out all its fields, please visit our [documentation](https://rvc4.docs.luxonis.com/software/ai-inference/nn-archive/#NN%20Archive-Configuration-Inputs).\n",
    "\n",
    "**Outputs**\n",
    "\n",
    "This section specifies all the model's output(s). It's defined as a list of dictionaries containing the name and data type of the output data. For more information, refer to our [documentation](https://rvc4.docs.luxonis.com/software/ai-inference/nn-archive/#NN%20Archive-Configuration-Outputs).\n",
    "\n",
    "**Head**\n",
    "\n",
    "This section configures the post-processing steps applied to the model's output(s). It's again defined as a list of dictionaries. Please visit our [documentation](https://rvc4.docs.luxonis.com/software/ai-inference/nn-archive/#NN%20Archive-Configuration-Heads) to learn more about it.\n",
    "\n",
    "**Metadata**\n",
    "\n",
    "This section specifies the name of the model, the path to it, and the model's precision.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The creation of a `NN Archive` looks like this:\n",
    "\n",
    "```python\n",
    "from luxonis_ml.nn_archive.archive_generator import ArchiveGenerator\n",
    "from luxonis_ml.nn_archive.config import CONFIG_VERSION\n",
    "\n",
    "\n",
    "config = {\n",
    "    \"config_version\": CONFIG_VERSION,       # Draw config version from luxonis-ml\n",
    "    \"model\": {\n",
    "        \"metadata\": { ... },                # Specify the model's metadata\n",
    "        \"inputs\":   [ { ... }, ... ],       # Specify the model's input stream(s)\n",
    "        \"outputs\":  [ { ... }, ... ],       # Specify the model's output stream(s)\n",
    "        \"heads\":    [ { ... }, ... ],       # Specify all heads for the model\n",
    " }\n",
    "}\n",
    "\n",
    "generator = ArchiveGenerator(\n",
    "    archive_name=\"...\",                     # Name of the generated archive file\n",
    "    save_path=\"...\",                        # Path to the \n",
    "    cfg_dict=config,\n",
    "    executables_paths=[\"...\"]\n",
    ")\n",
    "\n",
    "generator.make_archive()                    # Archive file is saved to the specified save_path\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"export-and-archive\"></a>\n",
    "\n",
    "## 🗂️ Export and Archive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we are satisfied with the model's performance, we want to prepare it for deployment on the device. This preparation consists of 2 steps. First, we want to export the model trained with PyTorch to a more general format called [`Open Neural Network Exchange (ONNX)`](https://onnx.ai/). Then, we want to package this exported model into a `NN Archive` as described in the section above.\n",
    "\n",
    "Let's start by exporting the model to ONNX."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "onnx_model_path = \"resnet18.onnx\"\n",
    "input_tensor = torch.randn(1, 3, 224, 224)  # Random input tensor\n",
    "\n",
    "torch.onnx.export(\n",
    "    model,                      # Model we want to export\n",
    "    input_tensor,               # Example input tensor\n",
    "    onnx_model_path,            # Path to save the ONNX model\n",
    "    input_names=[\"images\"],     # Input tensor names\n",
    "    output_names=[\"output\"],    # Output tensor names\n",
    "    opset_version=11            # ONNX opset version\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below creates the `NN Archive`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./resnet18.tar.xz'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from luxonis_ml.nn_archive import ArchiveGenerator\n",
    "from luxonis_ml.nn_archive.config_building_blocks import (\n",
    "    DataType,\n",
    "    InputType,\n",
    ")\n",
    "from luxonis_ml.nn_archive.config import CONFIG_VERSION\n",
    "\n",
    "# Define the configuration dictionary\n",
    "config = {\n",
    "    \"config_version\": CONFIG_VERSION, # draw config version from luxonis-ml\n",
    "    \"model\": {\n",
    "        \"metadata\": {\n",
    "            \"name\": \"resnet18\",\n",
    "            \"path\": \"resnet18.onnx\",\n",
    "            \"precision\": DataType.FLOAT32\n",
    "        },\n",
    "        \"inputs\": [ # Specify all inputs to the model\n",
    "            {\n",
    "                \"name\": \"images\",  # Define the input tensor name\n",
    "                \"dtype\": DataType.FLOAT32, # Define the input tensor data type\n",
    "                \"input_type\": InputType.IMAGE, \n",
    "                \"shape\": [1, 3, 224, 224], # Define the input tensor shape\n",
    "                \"layout\": \"NCHW\", # Define the input tensor order\n",
    "                \"preprocessing\": {\n",
    "                    \"mean\": [123.675, 116.28, 103.53], # Mean values for each channel applied during preprocessing\n",
    "                    \"scale\": [58.395, 57.12, 57.375] # Scale values for each channel applied during preprocessing\n",
    "                }\n",
    "            }\n",
    "        ],\n",
    "        \"outputs\": [  # Specify all outputs from the model\n",
    "            {\n",
    "                \"name\": \"output\", # Define the output tensor name\n",
    "                \"dtype\": DataType.FLOAT32, # Define the output tensor data type\n",
    "                \"shape\": [1, 1000] # Define the output tensor shape\n",
    "            }\n",
    "        ],\n",
    "        \"heads\": [ # Specify all heads for the model\n",
    "            {\n",
    "                \"parser\": \"ClassificationParser\", # Define the parser to use from depthai-nodes\n",
    "                \"metadata\": {\n",
    "                    \"postprocessor_path\": None,\n",
    "                    \"is_softmax\": False, # Whether the model output is softmaxed\n",
    "                    \"n_classes\": 1000, # Number of classes in the model\n",
    "                    \"classes\": labels # List of class labels\n",
    "                },\n",
    "                \"outputs\": [\"output\"] # Define the output tensor to use for the head\n",
    "            }\n",
    "        ]\n",
    "    }\n",
    "}\n",
    "\n",
    "archive = ArchiveGenerator(\n",
    "    archive_name=\"resnet18\", # Define string name of the generated archive.\n",
    "    save_path=\"./\", # Define string path to where you want to save the archive file.\n",
    "    cfg_dict=config,\n",
    "    executables_paths=[\"resnet18.onnx\"], # Define a list of string paths to relevant model executables.\n",
    ")\n",
    "archive.make_archive()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"deploy\"></a>\n",
    "\n",
    "## 🤖 Deploy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have successfully exported and archived the model, we aim to deploy it to the Luxonis device. The model's specific format depends on the Luxonis device series you have. We will show you how to use our [`ModelConverter`](https://github.com/luxonis/modelconverter) to convert the model as simply as possible. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!modelconverter hub login"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To convert the model, we can use either CLI or Python API. We'll show both options but use the latter. For more details, please look [here](https://github.com/luxonis/modelconverter?tab=readme-ov-file#online-usage).\n",
    "\n",
    "The call below will create a new model card inside your team on `HubAI` with the model file and details uploaded. It will further convert the model on the cloud to the selected target platform (e.g. [`RVC2`](https://rvc4.docs.luxonis.com/hardware/platform/rvc/rvc2/), [`RVC4`](https://rvc4.docs.luxonis.com/hardware/platform/rvc/rvc4/)) and download the converted model to your device. Choosing the target is as simple as setting a `target` argument in the `convert` function or using: \n",
    "\n",
    "- `modelconverter hub convert rvc2 ...` or\n",
    "- `modelconverter hub convert rvc4 ...`.\n",
    "\n",
    "Besides this, there are some platform-specific parameters. To check them out, please visit our [documentation](https://rvc4.docs.luxonis.com/software/ai-inference/conversion/rvc-conversion/offline/modelconverter/#ModelConverter-Parameters-Platform-Specific)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Model <span style=\"color: #008000; text-decoration-color: #008000\">'Resnet18'</span> created with ID <span style=\"color: #008000; text-decoration-color: #008000\">'45c926fe-07aa-46b8-814e-ab4cc93c83f0'</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Model \u001b[32m'Resnet18'\u001b[0m created with ID \u001b[32m'45c926fe-07aa-46b8-814e-ab4cc93c83f0'\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Model variant <span style=\"color: #008000; text-decoration-color: #008000\">'Resnet18 224x224'</span> created with ID <span style=\"color: #008000; text-decoration-color: #008000\">'5035ba7e-3f02-4767-abdc-f3bd5d5a0864'</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Model variant \u001b[32m'Resnet18 224x224'\u001b[0m created with ID \u001b[32m'5035ba7e-3f02-4767-abdc-f3bd5d5a0864'\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Model instance <span style=\"color: #008000; text-decoration-color: #008000\">'Resnet18 224x224 base instance'</span> created with ID <span style=\"color: #008000; text-decoration-color: #008000\">'59dec65d-075c-48be-97d7-619d0befa398'</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Model instance \u001b[32m'Resnet18 224x224 base instance'\u001b[0m created with ID \u001b[32m'59dec65d-075c-48be-97d7-619d0befa398'\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">File <span style=\"color: #008000; text-decoration-color: #008000\">'resnet18.tar.xz'</span> uploaded to model instance <span style=\"color: #008000; text-decoration-color: #008000\">'59dec65d-075c-48be-97d7-619d0befa398'</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "File \u001b[32m'resnet18.tar.xz'\u001b[0m uploaded to model instance \u001b[32m'59dec65d-075c-48be-97d7-619d0befa398'\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Model instance <span style=\"color: #008000; text-decoration-color: #008000\">'Resnet18 224x224 exported to rvc2'</span> created for rvc2 export with ID \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'4b1b6147-e634-464b-8532-d9a354cae3bf'</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Model instance \u001b[32m'Resnet18 224x224 exported to rvc2'\u001b[0m created for rvc2 export with ID \n",
       "\u001b[32m'4b1b6147-e634-464b-8532-d9a354cae3bf'\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Donwloaded <span style=\"color: #008000; text-decoration-color: #008000\">'resnet18-224x224-exported-to-rvc2/resnet18.rvc2.tar.xz'</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Donwloaded \u001b[32m'resnet18-224x224-exported-to-rvc2/resnet18.rvc2.tar.xz'\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from modelconverter import convert\n",
    "\n",
    "# =============================================================================\n",
    "# RVC2 conversion\n",
    "# =============================================================================\n",
    "converted_model = convert(\n",
    "    \"rvc2\", \n",
    "    path=\"resnet18.tar.xz\",\n",
    "    name=\"Resnet18\",\n",
    "    description_short=\"Pretrained Resnet18 on ImageNet\",\n",
    "    tasks=[\"CLASSIFICATION\"],\n",
    "    license_type=\"MIT\",\n",
    "    is_public=False\n",
    ")\n",
    "\n",
    "# Equivalent command using the CLI\n",
    "# !modelconverter hub convert rvc2 --path \"resnet18.tar.xz\" \\\n",
    "#                                 --name \"Resnet18\" \\\n",
    "#                                 --description-short \"Pretrained Resnet18 on ImageNet\" \\\n",
    "#                                 --tasks \"CLASSIFICATION\" \\\n",
    "#                                 --license-type \"MIT\" \\\n",
    "#                                 --no-is-public\n",
    "\n",
    "# =============================================================================\n",
    "# RVC4 conversion\n",
    "# =============================================================================\n",
    "# converted_model = convert(\n",
    "#     \"rvc4\", \n",
    "#     path=\"resnet18.tar.xz\",\n",
    "#     name=\"Resnet18\",\n",
    "#     description_short=\"Pretrained Resnet18 on ImageNet\",\n",
    "#     tasks=[\"CLASSIFICATION\"],\n",
    "#     license_type=\"MIT\",\n",
    "#     quantization_data=\"general\",\n",
    "#     is_public=False\n",
    "# )\n",
    "\n",
    "# Equivalent command using the CLI\n",
    "# !modelconverter hub convert rvc4 --path \"resnet18.tar.xz\" \\\n",
    "#                                 --name \"Resnet18\" \\\n",
    "#                                 --description-short \"Pretrained Resnet18 on ImageNet\" \\\n",
    "#                                 --tasks \"CLASSIFICATION\" \\\n",
    "#                                 --license-type \"MIT\" \\\n",
    "#                                 --quantization-data \"GENERAL\" \\\n",
    "#                                 --no-is-public"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see for ourselves that this call really created a new model card on `HubAI` with the exported model.\n",
    "\n",
    "<img src=\"./media/resnet_model_exported.png\" alt=\"Exported model on HubAI\" width=\"800\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(Optional)** We'll now show you how to convert the model to another platform. The process is very similar to the one we used before. However, since the command above already created a model card for our model, we will use it by setting the `model-id`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Model variant <span style=\"color: #008000; text-decoration-color: #008000\">'Resnet18 224x224'</span> created with ID <span style=\"color: #008000; text-decoration-color: #008000\">'8437eb83-e269-4663-af43-2e9527d3eb4c'</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Model variant \u001b[32m'Resnet18 224x224'\u001b[0m created with ID \u001b[32m'8437eb83-e269-4663-af43-2e9527d3eb4c'\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Model instance <span style=\"color: #008000; text-decoration-color: #008000\">'Resnet18 224x224 base instance'</span> created with ID <span style=\"color: #008000; text-decoration-color: #008000\">'6bf16d6a-e484-43ea-ac52-9c5b656a49b4'</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Model instance \u001b[32m'Resnet18 224x224 base instance'\u001b[0m created with ID \u001b[32m'6bf16d6a-e484-43ea-ac52-9c5b656a49b4'\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">File <span style=\"color: #008000; text-decoration-color: #008000\">'resnet18.tar.xz'</span> uploaded to model instance <span style=\"color: #008000; text-decoration-color: #008000\">'6bf16d6a-e484-43ea-ac52-9c5b656a49b4'</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "File \u001b[32m'resnet18.tar.xz'\u001b[0m uploaded to model instance \u001b[32m'6bf16d6a-e484-43ea-ac52-9c5b656a49b4'\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Model instance <span style=\"color: #008000; text-decoration-color: #008000\">'Resnet18 224x224 exported to rvc4'</span> created for rvc4 export with ID \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'2f6c7663-5de6-47c9-b5c6-dd349a14b248'</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Model instance \u001b[32m'Resnet18 224x224 exported to rvc4'\u001b[0m created for rvc4 export with ID \n",
       "\u001b[32m'2f6c7663-5de6-47c9-b5c6-dd349a14b248'\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Donwloaded <span style=\"color: #008000; text-decoration-color: #008000\">'resnet18-224x224-exported-to-rvc4/resnet18.rvc4.tar.xz'</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Donwloaded \u001b[32m'resnet18-224x224-exported-to-rvc4/resnet18.rvc4.tar.xz'\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# =============================================================================\n",
    "# RVC2 conversion\n",
    "# =============================================================================\n",
    "# converted_model = convert(\n",
    "#     \"rvc2\", \n",
    "#     path=\"resnet18.tar.xz\",\n",
    "#     name=\"Resnet18\",\n",
    "#     tasks=[\"CLASSIFICATION\"],\n",
    "#     model_id=\"45c926fe-07aa-46b8-814e-ab4cc93c83f0\"\n",
    "# )\n",
    "\n",
    "# Equivalent command using the CLI\n",
    "# !modelconverter hub convert rvc2 --path \"resnet18.tar.xz\" \\\n",
    "#                                 --name \"Resnet18\" \\\n",
    "#                                 --model-id \"45c926fe-07aa-46b8-814e-ab4cc93c83f0\"\n",
    "\n",
    "# =============================================================================\n",
    "# RVC4 conversion\n",
    "# =============================================================================\n",
    "converted_model = convert(\n",
    "    \"rvc4\",\n",
    "    path=\"resnet18.tar.xz\",\n",
    "    name=\"Resnet18\",\n",
    "    tasks=[\"CLASSIFICATION\"],\n",
    "    model_id=\"45c926fe-07aa-46b8-814e-ab4cc93c83f0\",\n",
    "    quantization_data=\"general\"\n",
    ")\n",
    "\n",
    "# Equivalent command using the CLI\n",
    "# !modelconverter hub convert rvc4 --path \"resnet18.tar.xz\" \\\n",
    "#                                 --name \"Resnet18\" \\\n",
    "#                                 --model-id \"45c926fe-07aa-46b8-814e-ab4cc93c83f0\" \\\n",
    "#                                 --quantization-data \"GENERAL\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have successfully converted our model for RVC2 and RVC4 devices, so let's test it on the camera! Please copy the path to the downloaded archive with the converted model from the output log of the appropriate code cell; we will use it in the next section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "MODEL_PATH = \"resnet18-224x224-exported-to-rvc2/resnet18.rvc2.tar.xz\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check out other possible ways to convert your model for our devices, please refer to our [documentation](https://rvc4.docs.luxonis.com/software/ai-inference/conversion/rvc-conversion/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"depthai-script\"></a>\n",
    "\n",
    "## 📷 DepthAI Script"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To test our model on one of our cameras, we need to have `DepthAI v3` and `Depthai Nodes` installed. Moreover, the following script must be run locally and requires a Luxonis device connected to your machine.\n",
    "\n",
    "Please select the correct image type depending on the target platform you want to test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import depthai as dai\n",
    "\n",
    "img_frame_type = dai.ImgFrame.Type.BGR888p # RVC2\n",
    "# img_frame_type = dai.ImgFrame.Type.BGR888i # RVC4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the script to run the model on the DepthAI device:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "from depthai_nodes import ParsingNeuralNetwork\n",
    "from depthai_nodes.ml.messages import Classifications\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def visualize_classification(frame: np.ndarray, message: Classifications) -> bool:\n",
    "    \"\"\"Visualizes the classification on the frame.\"\"\"\n",
    "    classes = message.classes[:2]\n",
    "    scores = message.scores[:2]\n",
    "    if frame.shape[0] < 128:\n",
    "        frame = cv2.resize(frame, (frame.shape[1] * 2, frame.shape[0] * 2))\n",
    "    for i, (cls, score) in enumerate(zip(classes, scores)):\n",
    "        cv2.putText(\n",
    "            frame,\n",
    "            f\"{cls}: {score:.2f}\",\n",
    "            (10, 20 + 20 * i),\n",
    "            cv2.FONT_HERSHEY_TRIPLEX,\n",
    "            0.5,\n",
    "            255,\n",
    "        )\n",
    "\n",
    "    cv2.imshow(\"Classification\", frame)\n",
    "    if cv2.waitKey(1) == ord(\"q\"):\n",
    "        cv2.destroyAllWindows()\n",
    "        return True\n",
    "\n",
    "    return False\n",
    "\n",
    "\n",
    "with dai.Pipeline() as pipeline:\n",
    "    cam = pipeline.create(dai.node.Camera).build()\n",
    "    nn_archive = dai.NNArchive(MODEL_PATH)\n",
    "    # Create the neural network node\n",
    "    nn_with_parser = pipeline.create(ParsingNeuralNetwork).build(\n",
    "        cam.requestOutput((224, 224), type=img_frame_type, fps=30), \n",
    "        nn_archive\n",
    "    )\n",
    "    # Create output queues\n",
    "    parser_output_queue = nn_with_parser.out.createOutputQueue()\n",
    "    frame_queue = nn_with_parser.passthrough.createOutputQueue()\n",
    "\n",
    "    # Start pipeline\n",
    "    pipeline.start()\n",
    "\n",
    "    while pipeline.isRunning():\n",
    "        # Get the frame\n",
    "        frame: dai.ImgFrame = frame_queue.get().getCvFrame()\n",
    "        # Get the parsed message containing the segmentation mask\n",
    "        parser_msg: dai.ImgFrame = parser_output_queue.get()\n",
    "        if visualize_classification(frame, parser_msg):\n",
    "            pipeline.stop()\n",
    "            break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"onnx-export\"></a>\n",
    "\n",
    "## 🗂️ Export without Archive (Optional)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to skip the model archiving and convert the model straight from `ONNX.` However, when running the model on the device, we'd need to define parsers and other parameters manually, so we recommend first creating the `NN Archive` and then converting the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from modelconverter import convert\n",
    "\n",
    "converted_model = convert(\n",
    "    \"rvc2\", \n",
    "    path=\"resnet18.onnx\",\n",
    "    name=\"Resnet18 ONNX\",\n",
    "    description_short=\"Pretrained Resnet18 on ImageNet\",\n",
    "    tasks=[\"CLASSIFICATION\"],\n",
    "    license_type=\"MIT\",\n",
    "    is_public=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yay! 🎉🎉🎉 Huge congratulations, you have successfully finished this tutorial in which you deployed a pre-trained ResNet18 classification model to our cameras!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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